108 lines
3.5 KiB
Python
108 lines
3.5 KiB
Python
from keras_core import initializers
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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from keras_core.optimizers import optimizer
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@keras_core_export(["keras_core.optimizers.Adagrad"])
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class Adagrad(optimizer.Optimizer):
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"""Optimizer that implements the Adagrad algorithm.
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Adagrad is an optimizer with parameter-specific learning rates,
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which are adapted relative to how frequently a parameter gets
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updated during training. The more updates a parameter receives,
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the smaller the updates.
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Args:
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learning_rate: Initial value for the learning rate:
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a floating point value,
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Defaults to 0.001.
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Note that `Adagrad` tends to benefit from higher initial
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learning rate values compared to other optimizers.
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To match the exact form in the original paper, use 1.0.
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initial_accumulator_value: Floating point value.
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Starting value for the accumulators (per-parameter
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momentum values).
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Must be non-negative.
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epsilon: Small floating point value used to maintain
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numerical stability.
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{{base_optimizer_keyword_args}}
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Reference:
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- [Duchi et al., 2011](
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http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf).
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"""
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def __init__(
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self,
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learning_rate=0.001,
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initial_accumulator_value=0.1,
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epsilon=1e-7,
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weight_decay=None,
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clipnorm=None,
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clipvalue=None,
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global_clipnorm=None,
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use_ema=False,
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ema_momentum=0.99,
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ema_overwrite_frequency=None,
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name="adagrad",
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):
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super().__init__(
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learning_rate=learning_rate,
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weight_decay=weight_decay,
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clipnorm=clipnorm,
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clipvalue=clipvalue,
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global_clipnorm=global_clipnorm,
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use_ema=use_ema,
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ema_momentum=ema_momentum,
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ema_overwrite_frequency=ema_overwrite_frequency,
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name=name,
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)
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self.initial_accumulator_value = initial_accumulator_value
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self.epsilon = epsilon
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def build(self, var_list):
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if self.built:
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return
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super().build(var_list)
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self._accumulators = []
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initializer = initializers.Constant(self.initial_accumulator_value)
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for var in var_list:
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self._accumulators.append(
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self.add_variable(
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shape=var.shape,
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initializer=initializer,
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dtype=var.dtype,
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name="accumulator",
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)
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)
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def update_step(self, gradient, variable, learning_rate):
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"""Update step given gradient and the associated model variable."""
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lr = ops.cast(learning_rate, variable.dtype)
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gradient = ops.cast(gradient, variable.dtype)
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accumulator = self._accumulators[self._get_variable_index(variable)]
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accumulator.assign(accumulator + gradient * gradient)
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variable.assign(
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variable - (lr * gradient / ops.sqrt(accumulator + self.epsilon))
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)
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"initial_accumulator_value": self.initial_accumulator_value,
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"epsilon": self.epsilon,
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}
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)
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return config
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Adagrad.__doc__ = Adagrad.__doc__.replace(
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"{{base_optimizer_keyword_args}}", optimizer.base_optimizer_keyword_args
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)
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